Bayesian model mixing for longitudinal dynamics of relativistic heavy-ion collisions

In this talk, we present a generalized parameterization of the longitudinal profiles in the 3D Monte Carlo (MC) Glauber model that incorporates both ``tilted'' and ``shifted'' scenarios using the Bayesian model-mixing technique(Bozek, 2010}. Local energy and momentum conservation impose constraints on the parameters related to the modification(Shen, 2020). We perform a Bayesian analysis of RHIC BES data using a (3+1)D framework that combines our modified 3D MC Glauber model with the hydrodynamic model MUSIC and the hadronic transport model UrQMD. We will demonstrate how rapidity-dependent flow observables, namely v_{1,2}(eta), impose constraints on the energy and net baryon distributions in the initial state model. Our analysis will also employ a non-parametric approach for constraining the QGP-specific shear and bulk viscosities as functions of temperature T and chemical potential mu_B.